A/B Testing Runtime Calculator

If possible, a test should not last too long – 1-2 weeks are ideal. However, depending on the effort and the test scenario, a duration of 4 weeks can also make sense.

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A / B Test Runtime Test

In order to be able to calculate the test duration, the following metrics are required for the 3 points described above:

  • How much traffic (number of visitors) is there monthly / daily To calculate a test duration, you need the traffic number for the website to be tested. In order to then specifically start a test with a certain number of variants, one should first be aware of how many visitors are available in total to be tested.
  • How much traffic should be included or excluded? Then you can determine how many visitors would be ideal for a test, or whether you want to exclude users from a test, for example.
  • How many variants should be tested? We always assume 2 variants (reference vs. variation), but with more traffic it is also possible to test further variations.

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Digital Loop-Workflow

1. Collect Ideas & Hypotheses

2. Prioritize Ideas & Hypotheses

3. Implement and Execute Campaigns

4. Evaluation & Analysis

Our Services in A/B Testing & Personalization

1. Target Definition

– Generation of hypotheses and identification of test targets

– Selecting the tools that fit your needs best (e.g. Google Optimize, Optimizely)

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2. Side Variations

– Creating side variations with consideration of the accurate number of variables for the detailed determination of the cause-effect-relationship.
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3. Execution of the A/B test & analysis of the results

– Setting up the measurement process and then collecting the data to test the hypothesis

– Evaluation of the results for the derivation of new measures for your website

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Our A/B Testing & Personalization Team

Yuri

Jhonatan Arcos

  • Full Stack Entwickler
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Wan-Yu Lee

  • 6 years of experience in data analytics & market research
  • Adobe Analytics certified expert
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John Munoz

  • 10+ Jahre experience in Digital Analytics, MarTech & Tech SEO
  • Google Analytics & Adobe Analytics expert

Vladimir Stashevskiy

  • 6 Jahre expericience in Digital Analytics, MarTech & Digital Marketing
  • Google Analytics expert

Discover more about A/B Testing & Personalization

A/B Test Significance Calculator

You want to know whether your test results are significant and therefore really informative.

Blog Articel

“Your key to success: A/B Testing & Personalization”

Google Presentation

Templates on the topic: “Conversion Rate Optimization with the right A/B Testing workflow”

Further questions about A/B Testing & Personalization

Which elements are tested during A/B testing?

A/B testing generally tests the following elements: Calls-to-action, headlines, images, text length, forms, menu bars. Our experienced experts will be happy to support you in planning and organizing the testing processes.

Is A/B testing only performed on websites?

Is A/B testing only performed on websites?

In addition to websites, A/B testing can also be performed for emails, PPC ads, and CTA buttons.

What is a null hypothesis?

A/B testing is actually used to examine hypotheses. There are two typical concepts in hypothesis testing: the null hypothesis and the alternative hypothesis. Usually, the null hypothesis indicates that the performance of the two variants A and B are identical, while the alternative hypothesis states that they are not.

How often should I run A/B tests?

There are different opinions regarding this matter, however we recommend continuous testing. You should have a clear goal and enough page visitors, in order to achieve statistical relevance within an acceptable period of time.

Client vs. Server Side Testing?

The first thing that differentiates your A/B testing requirements is client-side or server-side A/B testing. This aspect is often overlooked. Nevertheless, it should be chosen based on your needs.

  • Client-side: commonly used to optimize conversion rates in marketing or funnel, for example by creating page variations directly on the users’ browser.
  • Server-side: when you need to test more in depth in relation to the visual changes, such as products (features) or experience for engagement, retention and more.

Do A/B tests have negative effects on SEO?

Many mistakenly think that A/B tests could have a negative impact on SEO. The truth is, that websites rather improve through A/B tests which results in better ranking.

How many users do I need for trust-worthy testing?

The wrong interpretation of statistical significance is one of the most frequent and serious mistakes committed in A/B testing. Usually the minimum required traffic is calculated using the following key figures:

  • The conversion rate of our control variation (variation A)
  • Minimum difference between the conversion values of variations
  • Confidence level
  • Statistical “Power”

For a sample calculation please use our runtime calculator on this page.

Interested in our service?

Contact us!

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80538 München
089 – 41 61 47 83 0
089 – 41 61 47 83 4
contact@digital-loop.com

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